Detecção e Quantificação de Neurónios em C.elegans

Nesta dissertação é apresentado um estudo dos sistemas de processamento automático de imagem em contexto de um problema relacionado com a individualização de neurónios em imagens da nematoda C. elegans durante estudos relacionados com a doença de Parkinson. Apresenta-se uma breve introdução à anatomia do verme, uma introdução à doença de Parkinson e uso do C. elegans em estudos relacionados e também é feita a análise de artigos em contexto de processamento de imagem para contextualizar a situação atual de soluções para o problema de extração de características e regiões específicas. Neste projeto é desenvolvida uma pipeline com o auxilio do software CellProfiler para procurar uma resposta para o problema em questão.

Hyperspectral Mineral Identification using SVM and SOM

Remote sensing techniques involving hyperspectral imagery have applications in a number of sciences that study some aspects of the surface of the planet. The analysis of hyperspectral images is complex because of the large amount of information involved and the noise within that data. Investigating images with regard to identify minerals, rocks, vegetation and other materials is an application of hyperspectral remote sensing in the earth sciences. This thesis evaluates the performance of two classification and clustering techniques on hyperspectral images for mineral identification. Support Vector Machines (SVM) and Self-Organizing Maps (SOM) are applied as classification and clustering techniques, respectively. Principal Component Analysis (PCA) is used to prepare the data to be analyzed. The purpose of using PCA is to reduce the amount of data that needs to be processed by identifying the most important components within the data. A well-studied dataset from Cuprite, Nevada and a dataset of more complex data from Baffin Island were used to assess the performance of these techniques. The main goal of this research study is to evaluate the advantage of training a classifier based on a small amount of data compared to an unsupervised method. Determining the effect of feature extraction on the accuracy of the clustering and classification method is another goal of this research. This thesis concludes that using PCA increases the learning accuracy, and especially so in classification. SVM classifies Cuprite data with a high precision and the SOM challenges SVM on datasets with high level of noise (like Baffin Island).

Espaces de timbre générés par des réseaux profonds convolutionnels

Il est avant-tout question, dans ce mémoire, de la modélisation du timbre grâce à des algorithmes d'apprentissage machine. Plus précisément, nous avons essayé de construire un espace de timbre en extrayant des caractéristiques du son à l'aide de machines de Boltzmann convolutionnelles profondes. Nous présentons d'abord un survol de l'apprentissage machine, avec emphase sur les machines de Boltzmann convolutionelles ainsi que les modèles dont elles sont dérivées. Nous présentons aussi un aperçu de la littérature concernant les espaces de timbre, et mettons en évidence quelque-unes de leurs limitations, dont le nombre limité de sons utilisés pour les construire. Pour pallier à ce problème, nous avons mis en place un outil nous permettant de générer des sons à volonté. Le système utilise à sa base des plug-ins qu'on peut combiner et dont on peut changer les paramètres pour créer une gamme virtuellement infinie de sons. Nous l'utilisons pour créer une gigantesque base de donnée de timbres générés aléatoirement constituée de vrais instruments et d'instruments synthétiques. Nous entrainons ensuite les machines de Boltzmann convolutionnelles profondes de façon non-supervisée sur ces timbres, et utilisons l'espace des caractéristiques produites comme espace de timbre. L'espace de timbre ainsi obtenu est meilleur qu'un espace semblable construit à l'aide de MFCC. Il est meilleur dans le sens où la distance entre deux timbres dans cet espace est plus semblable à celle perçue par un humain. Cependant, nous sommes encore loin d'atteindre les mêmes capacités qu'un humain. Nous proposons d'ailleurs quelques pistes d'amélioration pour s'en approcher.

Analysis of scoliosis trunk deformities using ICA

This paper describes a method for analyzing scoliosis trunk deformities using Independent Component Analysis (ICA). Our hypothesis is that ICA can capture the scoliosis deformities visible on the trunk. Unlike Principal Component Analysis (PCA), ICA gives local shape variation and assumes that the data distribution is not normal. 3D torso images of 56 subjects including 28 patients with adolescent idiopathic scoliosis and 28 healthy subjects are analyzed using ICA. First, we remark that the independent components capture the local scoliosis deformities as the shoulder variation, the scapula asymmetry and the waist deformation. Second, we note that the different scoliosis curve types are characterized by different combinations of specific independent components.

Recurrence Quantification Analysis of System Signals for Detecting Tool and Chatter in Turning

In this thesis, the applications of the recurrence quantification analysis in metal cutting operation in a lathe, with specific objective to detect tool wear and chatter, are presented.This study is based on the discovery that process dynamics in a lathe is low dimensional chaotic. It implies that the machine dynamics is controllable using principles of chaos theory. This understanding is to revolutionize the feature extraction methodologies used in condition monitoring systems as conventional linear methods or models are incapable of capturing the critical and strange behaviors associated with the metal cutting process.As sensor based approaches provide an automated and cost effective way to monitor and control, an efficient feature extraction methodology based on nonlinear time series analysis is much more demanding. The task here is more complex when the information has to be deduced solely from sensor signals since traditional methods do not address the issue of how to treat noise present in real-world processes and its non-stationarity. In an effort to get over these two issues to the maximum possible, this thesis adopts the recurrence quantification analysis methodology in the study since this feature extraction technique is found to be robust against noise and stationarity in the signals.The work consists of two different sets of experiments in a lathe; set-I and set-2. The experiment, set-I, study the influence of tool wear on the RQA variables whereas the set-2 is carried out to identify the sensitive RQA variables to machine tool chatter followed by its validation in actual cutting. To obtain the bounds of the spectrum of the significant RQA variable values, in set-i, a fresh tool and a worn tool are used for cutting. The first part of the set-2 experiments uses a stepped shaft in order to create chatter at a known location. And the second part uses a conical section having a uniform taper along the axis for creating chatter to onset at some distance from the smaller end by gradually increasing the depth of cut while keeping the spindle speed and feed rate constant.The study concludes by revealing the dependence of certain RQA variables; percent determinism, percent recurrence and entropy, to tool wear and chatter unambiguously. The performances of the results establish this methodology to be viable for detection of tool wear and chatter in metal cutting operation in a lathe. The key reason is that the dynamics of the system under study have been nonlinear and the recurrence quantification analysis can characterize them adequately.This work establishes that principles and practice of machining can be considerably benefited and advanced from using nonlinear dynamics and chaos theory.

Analysis of System Signals Based on Cross Recurrence Method for System Dynamics Characterization

Natural systems are inherently non linear. Recurrent behaviours are typical of natural systems. Recurrence is a fundamental property of non linear dynamical systems which can be exploited to characterize the system behaviour effectively. Cross recurrence based analysis of sensor signals from non linear dynamical system is presented in this thesis. The mutual dependency among relatively independent components of a system is referred as coupling. The analysis is done for a mechanically coupled system specifically designed for conducting experiment. Further, cross recurrence method is extended to the actual machining process in a lathe to characterize the chatter during turning. The result is verified by permutation entropy method. Conventional linear methods or models are incapable of capturing the critical and strange behaviours associated with the dynamical process. Hence any effective feature extraction methodologies should invariably gather information thorough nonlinear time series analysis. The sensor signals from the dynamical system normally contain noise and non stationarity. In an effort to get over these two issues to the maximum possible extent, this work adopts the cross recurrence quantification analysis (CRQA) methodology since it is found to be robust against noise and stationarity in the signals. The study reveals that the CRQA is capable of characterizing even weak coupling among system signals. It also divulges the dependence of certain CRQA variables like percent determinism, percent recurrence and entropy to chatter unambiguously. The surrogate data test shows that the results obtained by CRQA are the true properties of the temporal evolution of the dynamics and contain a degree of deterministic structure. The results are verified using permutation entropy (PE) to detect the onset of chatter from the time series. The present study ascertains that this CRP based methodology is capable of recognizing the transition from regular cutting to the chatter cutting irrespective of the machining parameters or work piece material. The results establish this methodology to be feasible for detection of chatter in metal cutting operation in a lathe.

Document Image Segmentation and Compression using Artificial Neural Networks and Evolutionary Methods

The work is intended to study the following important aspects of document image processing and develop new methods. (1) Segmentation ofdocument images using adaptive interval valued neuro-fuzzy method. (2) Improving the segmentation procedure using Simulated Annealing technique. (3) Development of optimized compression algorithms using Genetic Algorithm and parallel Genetic Algorithm (4) Feature extraction of document images (5) Development of IV fuzzy rules. This work also helps for feature extraction and foreground and background identification. The proposed work incorporates Evolutionary and hybrid methods for segmentation and compression of document images. A study of different neural networks used in image processing, the study of developments in the area of fuzzy logic etc is carried out in this work

k-NN based On-Line Handwritten Character recognition system

On-line handwriting recognition has been a frontier area of research for the last few decades under the purview of pattern recognition. Word processing turns to be a vexing experience even if it is with the assistance of an alphanumeric keyboard in Indian languages. A natural solution for this problem is offered through online character recognition. There is abundant literature on the handwriting recognition of western, Chinese and Japanese scripts, but there are very few related to the recognition of Indic script such as Malayalam. This paper presents an efficient Online Handwritten character Recognition System for Malayalam Characters (OHR-M) using K-NN algorithm. It would help in recognizing Malayalam text entered using pen-like devices. A novel feature extraction method, a combination of time domain features and dynamic representation of writing direction along with its curvature is used for recognizing Malayalam characters. This writer independent system gives an excellent accuracy of 98.125% with recognition time of 15-30 milliseconds

Author Identification in Malayalam using n-grams

Author identification is the problem of identifying the author of an anonymous text or text whose authorship is in doubt from a given set of authors. The works by different authors are strongly distinguished by quantifiable features of the text. This paper deals with the attempts made on identifying the most likely author of a text in Malayalam from a list of authors. Malayalam is a Dravidian language with agglutinative nature and not much successful tools have been developed to extract syntactic & semantic features of texts in this language. We have done a detailed study on the various stylometric features that can be used to form an authors profile and have found that the frequencies of word collocations can be used to clearly distinguish an author in a highly inflectious language such as Malayalam. In our work we try to extract the word level and character level features present in the text for characterizing the style of an author. Our first step was towards creating a profile for each of the candidate authors whose texts were available with us, first from word n-gram frequencies and then by using variable length character n-gram frequencies. Profiles of the set of authors under consideration thus formed, was then compared with the features extracted from anonymous text, to suggest the most likely author.

Unconstrained Handwritten Malayalam Character Recognition using Wavelet Transform and Support vector Machine Classifier

This paper presents the application of wavelet processing in the domain of handwritten character recognition. To attain high recognition rate, robust feature extractors and powerful classifiers that are invariant to degree of variability of human writing are needed. The proposed scheme consists of two stages: a feature extraction stage, which is based on Haar wavelet transform and a classification stage that uses support vector machine classifier. Experimental results show that the proposed method is effective

Automated Transcription System for Malayalam Language

Malayalam is one of the 22 scheduled languages in India with more than 130 million speakers. This paper presents a report on the development of a speaker independent, continuous transcription system for Malayalam. The system employs Hidden Markov Model (HMM) for acoustic modeling and Mel Frequency Cepstral Coefficient (MFCC) for feature extraction. It is trained with 21 male and female speakers in the age group ranging from 20 to 40 years. The system obtained a word recognition accuracy of 87.4% and a sentence recognition accuracy of 84%, when tested with a set of continuous speech data.

A System for Offline Recognition of Handwritten Characters in Malayalam Script

In this paper, we propose a handwritten character recognition system for Malayalam language. The feature extraction phase consists of gradient and curvature calculation and dimensionality reduction using Principal Component Analysis. Directional information from the arc tangent of gradient is used as gradient feature. Strength of gradient in curvature direction is used as the curvature feature. The proposed system uses a combination of gradient and curvature feature in reduced dimension as the feature vector. For classification, discriminative power of Support Vector Machine (SVM) is evaluated. The results reveal that SVM with Radial Basis Function (RBF) kernel yield the best performance with 96.28% and 97.96% of accuracy in two different datasets. This is the highest accuracy ever reported on these datasets

Content Based Image Retrieval System for Malayalam Handwritten Characters

Content Based Image Retrieval is one of the prominent areas in Computer Vision and Image Processing. Recognition of handwritten characters has been a popular area of research for many years and still remains an open problem. The proposed system uses visual image queries for retrieving similar images from database of Malayalam handwritten characters. Local Binary Pattern (LBP) descriptors of the query images are extracted and those features are compared with the features of the images in database for retrieving desired characters. This system with local binary pattern gives excellent retrieval performance

Spectral clustering independent component analysis for tissue classification from brain MRI

A spectral angle based feature extraction method, Spectral Clustering Independent Component Analysis (SC-ICA), is proposed in this work to improve the brain tissue classification from Magnetic Resonance Images (MRI). SC-ICA provides equal priority to global and local features; thereby it tries to resolve the inefficiency of conventional approaches in abnormal tissue extraction. First, input multispectral MRI is divided into different clusters by a spectral distance based clustering. Then, Independent Component Analysis (ICA) is applied on the clustered data, in conjunction with Support Vector Machines (SVM) for brain tissue analysis. Normal and abnormal datasets, consisting of real and synthetic T1-weighted, T2-weighted and proton density/fluid-attenuated inversion recovery images, were used to evaluate the performance of the new method. Comparative analysis with ICA based SVM and other conventional classifiers established the stability and efficiency of SC-ICA based classification, especially in reproduction of small abnormalities. Clinical abnormal case analysis demonstrated it through the highest Tanimoto Index/accuracy values, 0.75/98.8%, observed against ICA based SVM results, 0.17/96.1%, for reproduced lesions. Experimental results recommend the proposed method as a promising approach in clinical and pathological studies of brain diseases

Identifying Decisive Features for Distinctive Analysis of Writings in Malayalam

This paper presents a writer identification scheme for Malayalam documents. As the accomplishment rate of a scheme is highly dependent on the features extracted from the documents, the process of feature selection and extraction is highly relevant. The paper describes a set of novel features exclusively for Malayalam language. The features were studied in detail which resulted in a comparative study of all the features. The features are fused to form the feature vector or knowledge vector. This knowledge vector is then used in all the phases of the writer identification scheme. The scheme has been tested on a test bed of 280 writers of which 50 writers having only one page, 215 writers with at least 2 pages and 15 writers with at least 4 pages. To perform a comparative evaluation of the scheme the test is conducted using WD-LBP method also. A recognition rate of around 95% was obtained for the proposed approach